2022-05-10

Article introduction

Title: “Peripheral Blood Mitochondrial DNA Copy Number Is Associated with Prostate Cancer Risk and Tumor Burden”

Authors: Weimin Zhou, Min Zhu, Ming Gui, Lihua Huang, Zhi Long, Li Wang, Hui Chen, Yinghao Yin, Xianzhen Jiang, Yingbo Dai, Yuxin Tang, Leye He, Kuangbiao Zhong

Goal: Determine if mtDNA is a predictor for prostate cancer

Flowchart for project flow

Data set overview

Loading

  • Dimensions of the raw data set: 392, 13

  • Stratified on Controls and PCa cases (attribute called Group)

  • Purpose of article: Predict PCa from other variables, mainly mtDNA

Cleaning

  • Check for duplicates

  • Filter for PCRsuccess

  • New dimensions: 387, 13

Augmenting

  • BMI- and DFI-classifier

  • New columns based on TNM-notation

  • Add “Group” as strings

  • New dimensions: 387, 18

Boxplot with continuous variables, any outliers?

Boxplot with discrete variables, any outliers?

Re-creating plot from the article

Article visualizationArticle visualization

Article visualization

A better biomarker for PCa?

Interesting finding during exploratory data analysis

Logistic regression, excl. PSA

Significant p-values:
Maybe the distribution of Dfi-classes are skewed?

Logistic regression, incl. PSA

Significant p-values:

Principal component analysis (PCA)

PCAPCAPCA

PCA

Conclusion

  • We can support the conclusion of the article, mtDNA is a biomarker for PCa (e.g, it is reproducible)
  • PSA levels seem to be an even better biomarker
  • Both of the above could be supported by logistic regression
  • Conclusion for PCA?
  • Some further research? Should be possible to do classification on Gleason scores/AJCC, should also be possible to do regression (albeit out of the scope of this course)